Software containers are changing the way distributed applications are executedand managed on cloud computing resources. Autoscaling allows containerizedapplications and services to run resiliently with high availability without the demandof user intervention. However, specifying an auto­scaling policy that can guaranteethat no performance violations will take place is an extremely hard task, and doomedto fail unless considerable care is taken. Existing autoscaling solutions try to solvethis problem but fail to consider application specific parameters when doing so, thuscausing poor resource utilization and/or unsatisfactory quality of service in certaindynamic workload scenarios.This thesis proposes an autoscaling solution that enables cloud native application toautoscale based on application specific parameters. The proposed solution consistsof a profiling strategy that detects key parameters that affect the performance ofautoscaling, and an autoscaling algorithm that automatically enforces autoscalingdecisions based on derived parameters from the profiling strategy.The proposed solution is compared and evaluated against the default auto­scalingfeature in Kubernetes during different realistic user scenarios. Results from thetesting scenarios indicate that the proposed solution, which uses application specificparameters, outperforms the default autoscaling feature of Kubernetes in resourceutilization while keeping SLO violations at a minimum